{"title":"Datadog vs LangSmith","slug":"datadog-vs-langsmith","tools":[{"name":"Datadog","slug":"datadog","category":"observability","type":"cloud","website":"https://www.datadoghq.com","pricing":"paid","pricing_tiers":["Free tier (5 hosts)","$15/host/mo Infrastructure","$31/host/mo APM","Custom Enterprise"],"open_source":false,"self_hosted":false,"sdk_languages":["python","javascript","go","java","ruby","csharp","php"],"frameworks":["langchain","openai-agents"],"agent_features":{"llm_tracing":true,"cost_tracking":true,"evaluation":false,"prompt_management":false,"real_time_monitoring":true},"compliance":["soc2","hipaa","gdpr","pci-dss","iso27001"],"best_for":"Full-stack observability at scale — infrastructure, APM, logs, and LLM tracing in one platform","limitations":"Expensive at scale; LLM observability is newer and less mature than dedicated tools like Langfuse; vendor lock-in on proprietary data format","verified_by":"editorial","last_verified":"2026-04-28","source_urls":{"docs":"https://docs.datadoghq.com","pricing":"https://www.datadoghq.com/pricing"}},{"name":"LangSmith","slug":"langsmith","category":"observability","type":"cloud","website":"https://smith.langchain.com","pricing":"freemium","pricing_tiers":["Free (5k traces)","$39/seat/mo Plus","Custom Enterprise"],"open_source":false,"self_hosted":false,"sdk_languages":["python","javascript","typescript"],"frameworks":["langchain"],"agent_features":{"llm_tracing":true,"cost_tracking":true,"evaluation":true,"prompt_management":true,"real_time_monitoring":true},"compliance":["soc2","gdpr"],"best_for":"Deep tracing and evaluation for LangChain-based agents — tightest integration with the LangChain ecosystem","limitations":"Heavily coupled to LangChain; no self-hosted option; closed-source; less useful if you're not using LangChain","verified_by":"editorial","last_verified":"2026-04-28","source_urls":{"docs":"https://docs.smith.langchain.com","pricing":"https://www.langchain.com/pricing"}}],"category":"observability","last_verified":"2026-05-09","body":"Datadog LLM Observability and LangSmith both provide deep agent chain tracing. They diverge on evaluation depth, prompt management, framework breadth, and deployment flexibility. LangSmith wins on evaluation pipelines, prompt engineering, and self-hosting. Datadog wins on infrastructure integration and anomaly detection.\n\n## Where LangSmith wins\n\n* **Comprehensive evaluation with offline experiments, online monitoring, and pairwise comparison.** LangSmith provides four evaluator types: LLM-as-judge, code-based rules, human review, and pairwise comparison. Offline evaluation runs against curated datasets with configurable repetitions and caching. Online evaluation monitors production traces in real-time with sampling rate controls. Failing production traces can be routed back into datasets for regression testing. Multi-turn conversation evaluation via \"threads\" captures agent dialogue quality across turns. Datadog provides automated topic clustering and anomaly detection on production traces. It does not document LLM-as-judge evaluators, dataset management, offline experiments, pairwise comparison, or structured human review workflows.\n\n* **Prompt engineering with versioning, playground, and deployment.** LangSmith provides prompt versioning, a playground for iteration, and team collaboration on prompt changes. Prompts are deployable artifacts that can be tested against datasets before production deployment. Datadog LLM Observability does not document prompt management, versioning, or playground features.\n\n* **Framework-agnostic with self-hosted deployment and compliance certifications.** LangSmith supports LangChain, LangGraph, OpenAI, Anthropic, CrewAI, Vercel AI SDK, and Pydantic AI with auto-instrumentation. It offers cloud, self-hosted, and hybrid deployment options with HIPAA, SOC 2 Type 2, and GDPR compliance. Datadog auto-instruments OpenAI, LangChain, Bedrock, and Anthropic via Python SDK only. Datadog is cloud-only—no self-hosted deployment for LLM traces containing sensitive prompts and completions.\n\n## Where Datadog wins\n\n* **Unified APM platform with infrastructure context.** Datadog LLM Observability integrates with the full Datadog observability stack: infrastructure monitoring, APM, log management, distributed tracing, and alerting. LLM traces appear alongside the infrastructure metrics of the services running them. When an agent slows down, Datadog can correlate LLM latency with CPU saturation, memory pressure, or network issues on the host. LangSmith is LLM-application-only—infrastructure monitoring, log aggregation, and host-level metrics require separate tooling.\n\n* **Automated anomaly detection and prompt injection scanning.** Datadog surfaces anomalies across span names, workflow types, and input/output topics automatically—no manual threshold configuration. Prompt injection detection and sensitive data scanning are built-in security features. LangSmith provides dashboards, alerts, and webhook-triggered online evaluations. It does not document automated anomaly detection or prompt injection scanning as built-in features.\n\n## The agentic difference\n\nLangSmith addresses the full agent development lifecycle: trace production runs, evaluate output quality with multiple evaluator types against datasets, iterate on prompts with versioning and A/B testing, and deploy improved versions. Evaluation and prompt management workflows drive agent improvement over time. Datadog provides production observability and anomaly detection. It leaves evaluation and prompt iteration to external tools.\n\nFor teams whose primary workflow is \"observe → evaluate → improve → deploy\" on agent systems, LangSmith provides the complete loop. For teams whose primary workflow is \"monitor agent infrastructure alongside everything else in Datadog,\" Datadog LLM Observability provides that integration.\n\n## When to pick which\n\n* **Pick LangSmith** when the primary workflow is agent quality improvement — evaluating outputs with LLM-as-judge and human review, running experiments against datasets, iterating on prompts, and deploying improved versions — or when self-hosting is required for compliance (HIPAA, SOC 2).\n\n* **Pick Datadog** when LLM agent observability must integrate with existing Datadog infrastructure monitoring and APM, the team prioritizes automated anomaly detection and security scanning, and evaluation and prompt management are handled by separate tools or not yet required."}